Research into identifying people according to how they use mouse-like input devices, has so far only weakly explored presumptions of the methods used--for example environmental influences or influences of the source of original data. According to the author's knowledge, no work has yet tried to reproduce or enhance some predecessor's work. The results of existing works are promising, but only loosely connected. In order to improve the above-mentioned situation, this doctoral thesis reviews existing works in the fi eld, provides theoretical foundations to better understand and further evolve this identi cation method, and also explores modi fications in feature selection algorithm. Based on this theoretical summary, the experimental part of this dissertation focuses on improving feature selection and on comparing three different user environments and their data. It also enhanced selected former research on the use of unrestricted movements. Experiments designed by the author are carried out and their results are discussed for each mentioned experimental part.